Heavy-load train operation control method and device, medium and electronic equipment

Abstract

The invention discloses a heavy-load train operation control method which comprises the following steps: carrying out stress analysis on each carriage in a target heavy-load train, and calculating the control force of each carriage according to the control force acting on each carriage, the coupler force between each carriage and the adjacent carriage and the basic resistance borne by each carriage; constructing a multi-mass-point longitudinal dynamic model of the target heavy-load train; parameters of the basic resistance model have uncertainty; based on the multi-particle longitudinal dynamic model, solving a non-parallel distribution compensation fuzzy control law and an additional control law to obtain an optimal guaranteed-performance composite nonlinear feedback controller, and establishing a speed tracking closed-loop control system based on the optimal guaranteed-performance composite nonlinear feedback controller; and a speed tracking closed-loop control system is adopted to adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-load train runs according to the expected speed. The running efficiency and the running safety of the heavy haul train can be improved.

Description

Technical Field

[0001] The present application relates to the field of automatic control of heavy-haul train operation, and specifically relates to a heavy-haul train operation control method, device, medium, and electronic device. Background Art

[0002] Compared with expensive air transportation and time-consuming sea transportation, railway transportation has the advantages of large transportation volume, high efficiency, and low cost. As an important part of it, heavy-haul railway transportation has received more and more attention. Heavy-haul railway transportation is the main transportation channel for important materials such as coal and minerals. Heavy-haul trains have large payloads and long lengths, and the operation lines include a large number of complex terrains such as slopes and curves, resulting in an even greater increase in driving difficulty. In order to reduce the work intensity of drivers and improve transportation efficiency and safety, it is necessary to develop heavy-haul train operation control technology.

[0003] Most of the existing research on heavy-haul train operation control uses a multi-particle model. The multi-particle model regards each carriage of the train as a particle. These particles are both independent and tightly combined through the coupler force between the front and rear cars. Due to the non-linear characteristics of the basic resistance, currently, the control algorithms based on multi-particles mainly include non-linear control algorithms such as sliding mode control, linear control algorithms based on the linearized model at the equilibrium point, such as the model predictive control method (Model Predictive Control, MPC), intermittent control method, linear quadratic regulator (Linear Quadratic Regulator, LQR), etc., and data-based control methods such as neural networks, genetic algorithms, and machine learning. The MPC and LQR algorithms solve multi-objective optimization problems such as energy consumption, coupler force, and speed tracking, but they are aimed at the nominal model and do not consider the problem of uncertain basic resistance parameters. The intermittent control and sliding mode control only focus on the speed tracking situation and do not constrain the coupler force. The data-based control method and the MPC method have a large amount of calculation and are difficult to use in actual vehicle-mounted controllers; the linearized model at the equilibrium point may lead to model mismatch and affect the control accuracy.

[0004] In addition, most of the existing research on the operation control of heavy-haul trains focuses on the steady-state performance of the operation control of heavy-haul trains, and little research has been done on the dynamic performance. However, fast dynamic response can improve the operation efficiency of heavy-haul trains, and zero overshoot can improve the operation safety of heavy-haul trains. The Composite Nonlinear Feedback (CNF) control technology is one of the commonly used control methods that can improve the dynamic performance and can handle the problem of controller input saturation. At present, some scholars have proposed a T-S fuzzy CNF control algorithm to solve the problem of model nonlinearity. However, this algorithm uses a T-S fuzzy controller based on PDC (Parallel Distributed Compensation) as the linear part of the CNF controller and does not consider problems such as system parameter uncertainty and multi-objective optimization.

[0005] In summary, the existing research on the operation control of heavy-haul trains does not provide an operation control method for heavy-haul trains that can achieve multi-objective optimization of vehicle speed tracking, coupler force constraint, and energy consumption while improving the dynamic control performance and robustness of the system. Summary of the Invention

[0006] The present application provides a heavy-haul train operation control method, device, medium, and electronic device, which can achieve the purpose of improving the operation efficiency and driving safety of heavy-haul trains.

[0007] According to the first aspect of the present application, a heavy-haul train operation control method is provided. The method includes:

[0008] Perform a force analysis on each carriage in the target heavy-haul train, and construct a multi-particle longitudinal dynamics model of the target heavy-haul train according to the control force acting on each carriage, the coupler force between each carriage and the adjacent carriage, and the basic resistance received by each carriage; wherein, the basic resistance model parameters have uncertainties;

[0009] Based on the multi-particle longitudinal dynamics model, solve the non-parallel distributed compensation fuzzy control law and the additional control law to obtain an optimal guaranteed performance composite nonlinear feedback controller, and establish a speed tracking closed-loop control system based on the optimal guaranteed performance composite nonlinear feedback controller;

[0010] Adopt the speed tracking closed-loop control system to adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-haul train travels at the expected speed; wherein, the control force is the electric braking force or the traction force.

[0011] According to the second aspect of the present application, a heavy-haul train operation control device is provided. The device includes:

[0012] A model construction module is configured to perform a force analysis on each carriage in a target heavy-haul train, and construct a multi-particle longitudinal dynamics model of the target heavy-haul train according to the control forces acting on the carriages, the coupler forces between the carriages and adjacent carriages, and the basic resistances received by the carriages; wherein, the parameters of the basic resistance model are uncertain.

[0013] A controller solving module is configured to, based on the multi-particle longitudinal dynamics model, solve a non-parallel distributed compensation fuzzy control law and an additional control law to obtain an optimal guaranteed performance composite non-linear feedback controller, and establish a speed tracking closed-loop control system based on the optimal guaranteed performance composite non-linear feedback controller.

[0014] A speed tracking module is configured to use the speed tracking closed-loop control system to adjust the control forces acting on the carriages based on the current speeds of the carriages and the relative displacements between the carriages and adjacent carriages, so that the target heavy-haul train runs at an expected speed; wherein, the control force is an electric braking force or a traction force.

[0015] According to a third aspect of the present invention, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the heavy-haul train operation control method as described in the embodiment of the present application.

[0016] According to a fourth aspect of the present invention, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the heavy-haul train operation control method as described in the embodiment of the present application.

[0017] According to a fifth aspect of the present application, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the heavy-haul train operation control method as described in the embodiment of the present application.

[0018] The technical solution of the present application constructs a multi-particle longitudinal dynamics model considering the uncertainty of the basic resistance model parameters, designs a non-parallel distributed compensation fuzzy control law and an additional control law to obtain an optimal guaranteed performance composite non-linear feedback controller, and establishes a speed tracking closed-loop control system based on the optimal guaranteed performance composite non-linear feedback controller. The speed tracking closed-loop control system is used to adjust the control forces acting on the carriages based on the current speeds of the carriages and the relative displacements between the carriages and adjacent carriages, so that the target heavy-haul train runs at an expected speed. The technical solution of the present application can improve the dynamic response performance of heavy-haul train operation control, and is beneficial to improving the operation efficiency and driving safety of heavy-haul trains.

[0019] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 is a flowchart of a heavy-haul train operation control method provided according to Embodiment 1;

[0022] Figure 2 is a flowchart of a heavy-haul train operation control method provided according to Embodiment 2;

[0023] Figure 3 is a schematic diagram of the force condition of a heavy-haul train provided according to an embodiment of the present application;

[0024] Figure 4 is a schematic structural diagram of a heavy-haul train operation control device provided in Embodiment 3 of the present application;

[0025] Figure 5 is a schematic structural diagram of an electronic device provided in Embodiment 4 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", "target", "candidate", etc. in the description, claims and the above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 It is a flowchart of a heavy-haul train operation control method provided according to Embodiment 1. This embodiment is applicable to controlling the operation process of a heavy-haul train, especially in the case of speed tracking of a running heavy-haul train. This method can be executed by a heavy-haul train operation control device, which is implemented in the form of hardware and / or software and can be integrated into an electronic device running this system.

[0030] As Figure 1 shown, the method includes:

[0031] S110. Analyze the forces on each carriage in the target heavy-haul train, and construct a multi-particle longitudinal dynamics model of the target heavy-haul train according to the control forces acting on each carriage, the coupler forces between each carriage and adjacent carriages, and the basic resistances received by each carriage; wherein, the parameters of the basic resistance model have uncertainties.

[0032] S120. Based on the multi-particle longitudinal dynamics model, solve the non-parallel distributed compensation fuzzy control law and the additional control law to obtain an optimal guaranteed cost composite nonlinear feedback controller, and establish a speed tracking closed-loop control system based on the optimal guaranteed cost composite nonlinear feedback controller.

[0033] S130. Use the speed tracking closed-loop control system to adjust the control forces acting on each carriage based on the current speeds of each carriage and the relative displacements between each carriage and adjacent carriages, so that the target heavy-haul train travels at an expected speed; wherein, the control force is an electric braking force or a traction force.

[0034] Among them, the target heavy-haul train refers to the heavy-haul train that needs to perform speed tracking. The target heavy-haul train includes multiple carriages, including a locomotive and freight carriages. The multi-particle longitudinal dynamics model is used to describe the non-linear motion characteristics of the target heavy-haul train in the longitudinal direction. The multi-particle longitudinal dynamics model takes into account the basic resistance, which includes air resistance and mechanical resistance. The basic resistance is non-linear, and its model parameters have uncertainties. It should be noted that here the longitudinal direction refers to the direction in which the target heavy-haul train travels forward. The multi-particle longitudinal dynamics model is non-linear.

[0035] In the process of constructing the multi-particle longitudinal dynamics model for the target heavy-haul train, each carriage of the target heavy-haul train is regarded as a particle. These particles are both independent and tightly combined through the coupler forces between adjacent carriages.

[0036] The multi-particle longitudinal dynamics model is used to design an optimal guaranteed performance composite non-linear feedback controller. The optimal guaranteed performance composite non-linear feedback controller can handle the problem of asymmetric saturation constraints of the controller input and is used to establish a speed tracking closed-loop control system for the target heavy-haul train. By adjusting the control force of the target heavy-haul train through the speed tracking closed-loop control system, the speed tracking of the target heavy-haul train can be achieved.

[0037] The optimal guaranteed performance composite non-linear feedback controller includes a non-parallel distributed compensation fuzzy control law and an additional control law. The design of the non-parallel distributed compensation fuzzy control law takes into account the non-linear characteristics of the multi-particle longitudinal dynamics model and the asymmetric saturation constraints of the control force. The non-parallel distributed compensation fuzzy control law is used to ensure the stability of the speed tracking closed-loop control system. The additional control law is obtained by selecting a non-linear function and is used to improve the response speed of the speed tracking closed-loop control system. The optimal guaranteed performance composite non-linear feedback controller in the technical solution of this application has the characteristics of simple form and small online calculation amount, which is convenient for practical application.

[0038] After the optimal guaranteed performance composite non-linear feedback controller is solved, based on the current speeds of each carriage in the target heavy-haul train and the relative displacements between each carriage and adjacent carriages, the control forces acting on each carriage are adjusted through the optimal guaranteed performance composite non-linear feedback controller so that the target heavy-haul train travels at the expected speed; among them, the control force is the electric braking force or the traction force.

[0039] In the technical solution of this application, by establishing a multi-particle longitudinal dynamics model considering the uncertainty of the basic resistance model parameters, designing a non-parallel distributed compensation fuzzy control law and an additional control law, an optimal guaranteed performance composite non-linear feedback controller is obtained, and a speed tracking closed-loop control system is established based on the optimal guaranteed performance composite non-linear feedback controller. By using the speed tracking closed-loop control system, the control force acting on each carriage is adjusted based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-haul train runs at the expected speed. The technical solution of this application can improve the dynamic response performance of heavy-haul train operation control, which is beneficial to improving the operation efficiency and traffic safety of heavy-haul trains.

[0040] Embodiment 2

[0041] Figure 2 It is a flowchart of the heavy-haul train operation control method provided according to Embodiment 2. This embodiment is further optimized on the basis of the above embodiment.

[0042] As Figure 2 shown, the method includes:

[0043] S210. Analyze the forces on each carriage in the target heavy-haul train, and construct a multi-particle longitudinal dynamics model of the target heavy-haul train according to the control force acting on each carriage, the coupler force between each carriage and the adjacent carriage, and the basic resistance received by each carriage; wherein, the parameters of the basic resistance model have uncertainties.

[0044] When the target heavy-haul train consists of l l locomotives and l w freight carriages, and l = l l + l w , in an optional embodiment, use to represent the multi-particle longitudinal dynamics model of the target heavy-haul train; wherein, q = 1,..., l; m q is the mass of the qth carriage, v q is the speed of the qth carriage, F q is the control force acting on the qth carriage, F in,q represents the coupler force between the qth carriage and the (q + 1)th carriage, and F Rg,q represents the basic resistance received by the qth carriage;

[0045] Among them, F in,q = kz in,q , q = 1,..., l - 1; q = 1,..., l; z in,q is the relative displacement between the qth and the (q + 1)th carriages, k is the spring stiffness coefficient, and g is the acceleration due to gravity. and is the basic resistance coefficient, is the nominal value of the basic resistance coefficient, is a variable parameter and satisfies Then it is the amplitude of the change in the basic resistance coefficient.

[0046] S220. Consider the locomotive in the target heavy-haul train and the first number of freight carriages behind the locomotive as a group, and simplify the multi-particle longitudinal dynamics model to obtain an error dynamics model.

[0047] Among them, the first number is determined according to the actual situation and is not limited here. Assume that the air resistance part in the basic resistance only acts on the locomotive, and the mechanical resistance part acts on all carriages. Figure 3 is a schematic diagram of the force on the heavy-haul train provided according to the embodiment of the present application. Refer to Figure 3 , consider the locomotive and the first number of freight carriages behind the locomotive as a group, and simplify the multi-particle longitudinal dynamics model of the target heavy-haul train.

[0048] In an optional embodiment, the multi-particle longitudinal dynamics model after grouping the carriages in the target heavy-haul train is represented by the following formula:

[0049]

[0050] In the formula, M p , v q , x in,p and F p are respectively the carriage mass, carriage speed, relative displacement between the pth group and the (p + 1)th group of carriages, and the control force acting on the pth group of carriages.

[0051] Furthermore, the multi-particle longitudinal dynamics model is transformed into:

[0052]

[0053] Among them, A(x), ΔA(x), B, and C are matrices of corresponding dimensions;

[0054] e v,p = v p - v r , e in,p = x in,p - x in,r , u e,p = F p - ueq,p (p = 1, …, l l ), l l is the number of locomotives in the target heavy-haul train, v r is the desired speed, x in,r is the desired relative displacement, u eq,p (p = 1, …, l l ) is the control force under the equilibrium state.

[0055] Among them,

[0056] S230. Convert the asymmetric control quantity saturation constraint of the error dynamics model into a symmetric constraint of the control quantity, and select the antecedent variables according to the nonlinear terms in the error dynamics model to obtain the T-S fuzzy dynamics error model.

[0057] After simplifying the multi-particle longitudinal dynamics model to obtain the error dynamics model, define the maximum values of the electric braking force and the traction force as F min,p and F max,p , and F min,p ≠F max,p ,

[0058] The following constraints need to be satisfied: -F min,p ≤F p ≤F max,p ,

[0059] Define the new control quantity Then there is

[0060] Define the symmetric constraint of the control quantity as:

[0061] Among them, Then the error dynamics model can be transformed into:

[0062]

[0063] Among them, the control input variable B w = B,

[0064] Define the antecedent variable ξ = e v,1 Then the error dynamics model can be further transformed into:

[0065]

[0066] Let ξ min ≤ξ≤ξ max, represent ξ as ξ = h 1 (ξ)ξ min +h 2 (ξ)ξ max ,

[0067] where

[0068] Regarding a part of the non - linear system as a non - linear characteristic satisfying a specific sector condition, the T - S fuzzy dynamic error model can be expressed by the following formula:

[0069]

[0070] where A i , B, B w , C are system matrices of corresponding dimensions.

[0071] S240. Based on the T - S fuzzy dynamic error model, solve the non - parallel distributed compensation fuzzy control law and the additional control law to obtain an optimal guaranteed - cost composite non - linear feedback controller, and establish a speed - tracking closed - loop control system based on the optimal guaranteed - cost composite non - linear feedback controller.

[0072] where the optimal guaranteed - cost composite non - linear feedback controller includes a non - parallel distributed compensation fuzzy control law and an additional control law.

[0073] In an alternative embodiment, use the formula to represent the optimal guaranteed - cost composite non - linear feedback controller.

[0074] where u non-PDC is the non - parallel distributed compensation fuzzy control law, and u add is the additional control law;

[0075]

[0076] where

[0077] Optionally, the speed - tracking closed - loop control system is expressed by the following formula:

[0078]

[0079] where

[0080] To achieve multi - objective optimization of cruise speed tracking, coupler force, and energy consumption, define the following objective function:

[0081]

[0082] where tf is the terminal time, and the coupler force W u , W ev and W f are the weighting matrices of the control quantity, speed tracking error, and coupler force, respectively.

[0083] The design objectives of the optimal guaranteed performance composite nonlinear feedback controller are as follows: 1) When w = 0, the above closed-loop system is asymptotically stable; 2) When w ≠ 0, the above closed-loop system satisfies the H ∞ performance index, that is, ‖y‖ 2 ≤γ‖w‖ 2 , where γ is the disturbance rejection index; 3) The upper bound of the above objective function exists and is minimized.

[0084] To achieve the above design objectives, it is necessary to solve the following convex optimization problem to obtain the controller gains K n and H n . The convex optimization problem is as follows:

[0085] Given scalars τ > 0, χ i > 0, c 0 > 0, and 0 < ε p < 1, in the region the T-S fuzzy system with parameter uncertainties is asymptotically stable, the upper bound of the cost function exists, and it satisfies the H ∞ performance if there exist matrices q = Q T > 0, H j > 0, K j (j = 1, 2), and Y = Y T > 0, and the H ∞ performance index γ > 0 such that the following optimization problem has a feasible solution:

[0086]

[0087] where,

[0088]

[0089] Π = -τ -1 θ -1 , is the p-th row of the matrix K j .

[0090] In the above inequalities, the known parameters include u max,p , τ, χ i , c 0 and ε p , the defined system matrices A i , B, Bw , and and the given weighted matrix W u , W ev and W f , the matrices to be solved include H j , K j , Q and Y.

[0091] In an alternative embodiment, u add = ψ(y)B T Px;

[0092] where the non - linear functions β p and λ p are both adjustable parameters, and P is a Lyapunov matrix.

[0093] Then, the expression of the optimal guaranteed - cost composite non - linear feedback controller is:

[0094]

[0095] S250. Adopt the speed - tracking closed - loop control system, and adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy - haul train travels at the expected speed; wherein, the control force is the electric braking force or the traction force.

[0096] The embodiment of the present application considers the non - linear characteristics of the multi - particle longitudinal dynamics model and the asymmetric saturation constraint of the control force, designs the non - parallel distributed compensation fuzzy control law of the optimal guaranteed - cost composite non - linear feedback controller to ensure the stability of the speed - tracking closed - loop control system, selects non - linear functions, and designs the additional control law of the optimal guaranteed - cost composite non - linear feedback controller, which is beneficial to improving the dynamic performance of the speed - tracking closed - loop control system, realizing the multi - objective optimization of fast vehicle speed tracking, cruise speed tracking, coupler force and energy consumption. The optimal guaranteed - cost composite non - linear feedback controller has the characteristics of simple form and small online calculation amount, which is convenient for practical application.

[0097] The heavy - haul train operation control method proposed in the embodiment of the present application can well track the desired speed, the control quantity is within the saturation constraint range, the coupler force is small, and it has faster dynamic performance.

[0098] Embodiment III

[0099] Figure 4FIG. 0 is a schematic structural diagram of the heavy-haul train operation control device provided in Embodiment 3 of the present application. In this embodiment, the operation process of the heavy-haul train is controlled, especially in the case of speed tracking of the running heavy-haul train. The device can be implemented by software and / or hardware and can be integrated into electronic devices such as smart terminals.

[0100] As Figure 4 shown, the heavy-haul train operation control device 400 may include:

[0101] A model construction module 410, configured to perform a force analysis on each carriage in the target heavy-haul train, and construct a multi-particle longitudinal dynamics model of the target heavy-haul train according to the control force acting on each carriage, the coupler force between each carriage and the adjacent carriage, and the basic resistance received by each carriage; wherein, the basic resistance model parameters have uncertainties;

[0102] A controller solution module 420, configured to solve a non-parallel distributed compensation fuzzy control law and an additional control law based on the multi-particle longitudinal dynamics model to obtain an optimal guaranteed performance composite non-linear feedback controller, and establish a speed tracking closed-loop control system based on the optimal guaranteed performance composite non-linear feedback controller;

[0103] A speed tracking module 430, configured to use the speed tracking closed-loop control system to adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-haul train travels at an expected speed; wherein, the control force is an electric braking force or a traction force.

[0104] The technical solution of the present application is to establish a multi-particle longitudinal dynamics model considering the uncertainties of the basic resistance model parameters, design a non-parallel distributed compensation fuzzy control law and an additional control law to obtain an optimal guaranteed performance composite non-linear feedback controller, and establish a speed tracking closed-loop control system based on the optimal guaranteed performance composite non-linear feedback controller. The speed tracking closed-loop control system is used to adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-haul train travels at an expected speed. The technical solution of the present application can improve the dynamic response performance of heavy-haul train operation control, which is beneficial to improving the operation efficiency and driving safety of heavy-haul trains.

[0105] Optionally, the controller solving module 420 includes: a model simplification sub-module, configured to regard the locomotive in the target heavy-haul train and the first number of freight carriages behind the locomotive as a group, simplify the multi-particle longitudinal dynamics model, and obtain an error dynamics model; a model transformation sub-module, configured to convert the asymmetric control quantity saturation constraint of the error dynamics model into a symmetric constraint of the control quantity, and select the antecedent variables according to the non-linear terms in the error dynamics model to obtain a T-S fuzzy dynamics error model; a controller solving sub-module, configured to solve the non-parallel distributed compensation fuzzy control law and the additional control law based on the T-S fuzzy dynamics error model to obtain an optimal guaranteed performance composite non-linear feedback controller.

[0106] Optionally, the model construction module 410 is specifically configured to: adopt to represent the multi-particle longitudinal dynamics model of the target heavy-haul train; where q = 1,..., l; m q is the mass of the q-th carriage, v q is the speed of the q-th carriage, F q is the control force acting on the q-th carriage, F in,q represents the coupler force between the q-th carriage and the (q + 1)-th carriage, F Rg,q represents the basic resistance received by the q-th carriage;

[0107] where F in,q = kz in,q , q = 1,..., l - 1; z in,q is the relative displacement between the q-th and the (q + 1)-th carriages, k is the spring stiffness coefficient, g is the acceleration due to gravity, and are the basic resistance coefficients, characterize the unknown parameters and satisfy and then is the variation amplitude of the basic resistance coefficient.

[0108] Optionally, the multi-particle longitudinal dynamics model after grouping the carriages in the target heavy-haul train is represented by the following formula:

[0109]

[0110] In the formula, M p , v q , x in,p and F p are respectively the mass of the p-th group of carriages, the speed of the carriages, the relative displacement between the p-th group and the (p + 1)-th group of carriages, and the control force acting on the p-th group of carriages;

[0111] Furthermore, transform the multi-particle longitudinal dynamics model into:

[0112]

[0113] wherein, A(x), ΔA(x), B, and C are matrices of corresponding dimensions;

[0114] e v,p = v p - v r e in,p = x in,p - x in,r u e,p = F p - u eq,p (p = 1, …, l l ) and l l is the number of locomotives in the target heavy-haul train, v r is the desired speed, x in,r is the desired relative displacement, and u eq,p (p = 1, …, l l ) is the control force under the equilibrium state.

[0115] Optionally, the T-S fuzzy dynamics error model is expressed by the following formula:

[0116]

[0117] wherein, the antecedent variable ξ = e v,1 , the control input variable the new control quantity F min,p and F max,p are respectively the maximum values of the electric braking force and the traction force, and F min,p ≠ F max,p , w is an additional term generated by the asymmetric constraint of the control quantity and parameter uncertainty, and A i , ΔA i , B, B w are system matrices of corresponding dimensions.

[0118] Optionally, the controller solving sub-module is specifically used to represent the optimal guaranteed cost composite nonlinear feedback controller by the formula wherein, u

[0119] is the non-parallel distributed compensation fuzzy control law, and u non-PDC is the additional control law; add

[0120] ​

[0121] Among them,

[0122] u add = ψ(y)B T Px;

[0123] Among them, the non - linear function β p and λ p are both adjustable parameters, and P is a Lyapunov matrix.

[0124] The heavy - haul train operation control device provided by the invention embodiment can execute the heavy - haul train operation control method provided by any embodiment of the present application, and has the corresponding performance modules and beneficial effects for executing the heavy - haul train operation control method.

[0125] Embodiment 4

[0126] According to the embodiments of the present application, the present application also provides an electronic device, a readable storage medium, and a computer program product.

[0127] Figure 5 FIG. shows a schematic structural diagram of an electronic device 510 that can be used to implement the embodiments. The electronic device 510 includes at least one processor 511, and a memory communicatively connected to at least one processor 511, such as a read - only memory (ROM) 512, a random - access memory (RAM) 513, etc. Among them, the memory stores a computer program executable by at least one processor. The processor 511 can execute various appropriate actions and processes according to the computer program stored in the read - only memory (ROM) 512 or the computer program loaded from the storage unit 518 into the random - access memory (RAM) 513. In the RAM 513, various programs and data required for the operation of the electronic device 510 can also be stored. The processor 511, the ROM 512, and the RAM 513 are connected to each other through a bus 514. The input / output (I / O) interface 515 is also connected to the bus 514.

[0128] Multiple components in the electronic device 510 are connected to the I / O interface 515, including: an input unit 516, such as a keyboard, a mouse, etc.; an output unit 517, such as various types of displays, speakers, etc.; a storage unit 518, such as a disk, an optical disc, etc.; and a communication unit 519, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 519 allows the electronic device 510 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0129] The processor 511 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 511 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 511 executes the various methods and processes described above, such as the heavy-haul train operation control method.

[0130] In some embodiments, the heavy-haul train operation control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 518. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 510 via the ROM 512 and / or the communication unit 519. When the computer program is loaded into the RAM 513 and executed by the processor 511, one or more steps of the heavy-haul train operation control method described above can be executed. Alternatively, in other embodiments, the processor 511 can be configured to execute the heavy-haul train operation control method by any other suitable means (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] The computer program for implementing the method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable heavy-haul train operation control devices, such that when the computer program is executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer program can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0133] In the context of this application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0134] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0135] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a heavy-haul train operation control server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0136] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0137] An embodiment of the present application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the heavy-haul train operation control method provided in any embodiment of the present application. This program product and the heavy-haul train operation control methods disclosed in various embodiments of the present application belong to the same inventive concept, and thus will not be elaborated herein.

[0138] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved, and no limitation is made herein.

[0139] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A heavy-load train operation control method, characterized in that: The method comprises: Performing force analysis on each carriage in the target heavy-load train, and constructing a multi-particle longitudinal dynamic model of the target heavy-load train according to the control force acting on each carriage, the coupling force between each carriage and the adjacent carriage, and the basic resistance of each carriage; wherein the parameters of the basic resistance model have uncertainty; Based on the multi-particle longitudinal dynamics model, a non-parallel distributed compensation fuzzy control law and an additional control law are solved to obtain an optimal guaranteed cost composite nonlinear feedback controller, and a speed tracking closed-loop control system is established based on the optimal guaranteed cost composite nonlinear feedback controller; The speed tracking closed-loop control system is adopted to adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-load train travels at the expected speed; wherein the control force is electric braking force or traction force.

2. The method according to claim 1, characterized in that: The method of solving the non-parallel distributed compensation fuzzy control law and the additional control law based on the multi-particle longitudinal dynamics model to obtain the optimal guaranteed cost composite nonlinear feedback controller includes: The locomotive in the target heavy-load train and a first number of freight cars located behind the locomotive are regarded as a group, and the multi-particle longitudinal dynamics model is simplified to obtain an error dynamics model; The asymmetric control quantity saturation constraint of the error dynamics model is converted into a control quantity symmetric constraint, and antecedent variables are selected according to nonlinear terms in the error dynamics model to obtain a TS fuzzy dynamics error model; Based on the TS fuzzy dynamic error model, the non-parallel distributed compensation fuzzy control law and the additional control law are solved to obtain the optimal guaranteed cost composite nonlinear feedback controller.

3. The method according to claim 1 or 2, characterized in that: The force analysis of each carriage in the target heavy-load train is performed, and a multi-mass point longitudinal dynamic model of the target heavy-load train is constructed according to the control force acting on each carriage, the coupling force between each carriage and the adjacent carriage, and the basic resistance of each carriage, including: use represents the multi-particle longitudinal dynamic model of the target heavy-load train; wherein, q=1,…,l; m q is the mass of the qth carriage, v q is the speed of the qth carriage, F q is the control force acting on the qth carriage, F in,q represents the coupling force between the qth car and the q+1th car, F Rg,q represents the basic resistance of the qth carriage; Among them, F in,q =kz in,q ,q=1,…,l-1; q=1,…,l;z in,q is the relative displacement between the qth and q+1th carriages, k is the spring stiffness coefficient, g is the acceleration due to gravity, and is the basic drag coefficient, Characterize unknown parameters and satisfy is the variation amplitude of the basic resistance coefficient.

4. The method according to claim 3, characterized in that The multi-particle longitudinal dynamics model after the carriages in the target heavy-load train are grouped is expressed by the following formula: Where M p 、v q 、x in,p and F p are the mass of the p-th group of carriages, the speed of the carriages, the relative displacement of the p-th group and the p+1-th group of carriages, and the control force acting on the p-th group of carriages; Furthermore, the multi-particle longitudinal dynamics model is transformed into: in, A(x), ΔA(x), B and C are matrices of corresponding dimensions; e v,p =v p -v r , e in,p =x in,p -x in,r ,u e,p =F p -u eq,p (p=1,…,l l ), l l is the number of locomotives in the target heavy-load train, v r is the expected speed, x in,r is the expected relative displacement, u eq,p (p=1,…,l l ) is the control force in equilibrium state.

5. The method according to claim 4, characterized in that The TS fuzzy dynamics error model is expressed by the following formula: Wherein, the antecedent variable ξ=e v,1 , control input variables New control volume F min,p and F max,p are the maximum values ​​of electric braking force and traction force respectively, and F min,p ≠F max,p , w is the additional term generated by the asymmetric constraint of the control quantity and the parameter uncertainty, A i , ΔA i , B, B w , C is the system matrix of the corresponding dimension.

6. The method according to claim 5, characterized in that The method of solving the non-parallel distributed compensation fuzzy control law and the additional control law based on the TS fuzzy dynamic error model to obtain the optimal guaranteed cost composite nonlinear feedback controller includes: Using formula represents the optimal guaranteed cost composite nonlinear feedback controller; Among them, u non-PDC is the non-parallel distributed compensation fuzzy control law, u add is the additional control law; in, u add =ψ(y)B T Px; Among them, the nonlinear function β p and λ p are all adjustable parameters, and P is the Lyapunov matrix.

7. A heavy-load train operation control device, characterized in that: The device comprises: A model building module is used to perform force analysis on each carriage in the target heavy-load train, and to build a multi-particle longitudinal dynamic model of the target heavy-load train according to the control force acting on each carriage, the coupling force between each carriage and the adjacent carriage, and the basic resistance of each carriage; wherein the basic resistance model parameters have uncertainty; A controller solving module is used to solve the non-parallel distributed compensation fuzzy control law and the additional control law based on the multi-particle longitudinal dynamics model to obtain an optimal guaranteed cost composite nonlinear feedback controller, and establish a speed tracking closed-loop control system based on the optimal guaranteed cost composite nonlinear feedback controller; A speed tracking module is used to use the speed tracking closed-loop control system to adjust the control force acting on each carriage based on the current speed of each carriage and the relative displacement between each carriage and the adjacent carriage, so that the target heavy-load train travels at the expected speed; wherein the control force is electric braking force or traction force.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the heavy-load train operation control method as described in any one of claims 1 to 6 is implemented.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the heavy-load train operation control method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the heavy-load train operation control method according to any one of claims 1 to 6.

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